Distributed Synthesis in Continuous Time

We introduce a formalism modelling communication of distributed agents strictly in continuous-time. Within this framework, we study the problem of synthesising local strategies for individual agents such that a specified set of goal states is reached, or reached with at least a given probability. The flow of time is modelled explicitly based on continuous-time randomness, with two natural implications: First, the non-determinism stemming from interleaving disappears. Second, when we restrict to a subclass of non-urgent models, the quantitative value problem for two players can be solved in EXPTIME. Indeed, the explicit continuous time enables players to communicate their states by delaying synchronisation (which is unrestricted for non-urgent models). In general, the problems are undecidable already for two players in the quantitative case and three players in the qualitative case. The qualitative undecidability is shown by a reduction to decentralized POMDPs for which we provide the strongest (and rather surprising) undecidability result so far

Real-Time Synthesis is Hard!

We study the reactive synthesis problem (RS) for specifications given in Metric Interval Temporal Logic (MITL). RS is known to be undecidable in a very general setting, but on infinite words only; and only the very restrictive BRRS subcase is known to be decidable (see D'Souza et al. and Bouyer et al.). In this paper, we precise the decidability border of MITL synthesis. We show RS is undecidable on finite words too, and present a landscape of restrictions (both on the logic and on the possible controllers) that are still undecidable. On the positive side, we revisit BRRS and introduce an efficient on-the-fly algorithm to solve it

Adaptive Real Time Imaging Synthesis Telescopes

The digital revolution is transforming astronomy from a data-starved to a data-submerged science. Instruments such as the Atacama Large Millimeter Array (ALMA), the Large Synoptic Survey Telescope (LSST), and the Square Kilometer Array (SKA) will measure their accumulated data in petabytes. The capacity to produce enormous volumes of data must be matched with the computing power to process that data and produce meaningful results. In addition to handling huge data rates, we need adaptive calibration and beamforming to handle atmospheric fluctuations and radio frequency interference, and to provide a user environment which makes the full power of large telescope arrays accessible to both expert and non-expert users. Delayed calibration and analysis limit the science which can be done. To make the best use of both telescope and human resources we must reduce the burden of data reduction. Our instrumentation comprises of a flexible correlator, beam former and imager with digital signal processing closely coupled with a computing cluster. This instrumentation will be highly accessible to scientists, engineers, and students for research and development of real-time processing algorithms, and will tap into the pool of talented and innovative students and visiting scientists from engineering, computing, and astronomy backgrounds. Adaptive real-time imaging will transform radio astronomy by providing real-time feedback to observers. Calibration of the data is made in close to real time using a model of the sky brightness distribution. The derived calibration parameters are fed back into the imagers and beam formers. The regions imaged are used to update and improve the a-priori model, which becomes the final calibrated image by the time the observations are complete

Microarchitecture optimization for timing and layout

In recent years the drive to produce more complex integrated circuits while spending less design time has driven the demand for design automation tools. The search for design automation methods has resulted in the design of numerous behavioral synthesis and logic synthesis tools. This report describes a system that fills the gap between traditional behavioral synthesis and logic synthesis tools. Techniques are introduced for improving the microarchitecture structure and using feedback from lower-level optimization tools to guide design optimizations while attempting to meet user specified area and time constraints. These techniques include the capability for mixing layout styles such as custom layout for random-logic components and bit-slicing for regularly structured components. In this manner the entire design, control logic and datapath, can be optimized at the same time. Further, this paper presents a new methodology for microarchitecture-level optimization that greatly reduces the amount of technology-specific knowledge necessary to perform the optimizations

Two-View Matching with View Synthesis Revisited

Wide-baseline matching focussing on problems with extreme viewpoint change is considered. We introduce the use of view synthesis with affine-covariant detectors to solve such problems and show that matching with the Hessian-Affine or MSER detectors outperforms the state-of-the-art ASIFT. To minimise the loss of speed caused by view synthesis, we propose the Matching On Demand with view Synthesis algorithm (MODS) that uses progressively more synthesized images and more (time-consuming) detectors until reliable estimation of geometry is possible. We show experimentally that the MODS algorithm solves problems beyond the state-of-the-art and yet is comparable in speed to standard wide-baseline matchers on simpler problems. Minor contributions include an improved method for tentative correspondence selection, applicable both with and without view synthesis and a view synthesis setup greatly improving MSER robustness to blur and scale change that increase its running time by 10% only.Comment: 25 pages, 14 figure

Finite State Machine Synthesis for Evolutionary Hardware

This article considers application of genetic algorithms for finite machine synthesis. The resulting genetic finite state machines synthesis algorithm allows for creation of machines with less number of states and within shorter time. This makes it possible to use hardware-oriented genetic finite machines synthesis algorithm in autonomous systems on reconfigurable platforms

Dynamic Bayesian Predictive Synthesis in Time Series Forecasting

We discuss model and forecast combination in time series forecasting. A foundational Bayesian perspective based on agent opinion analysis theory defines a new framework for density forecast combination, and encompasses several existing forecast pooling methods. We develop a novel class of dynamic latent factor models for time series forecast synthesis; simulation-based computation enables implementation. These models can dynamically adapt to time-varying biases, miscalibration and inter-dependencies among multiple models or forecasters. A macroeconomic forecasting study highlights the dynamic relationships among synthesized forecast densities, as well as the potential for improved forecast accuracy at multiple horizons

Synthesis of Covert Actuator Attackers for Free

In this paper, we shall formulate and address a problem of covert actuator attacker synthesis for cyber-physical systems that are modelled by discrete-event systems. We assume the actuator attacker partially observes the execution of the closed-loop system and is able to modify each control command issued by the supervisor on a specified attackable subset of controllable events. We provide straightforward but in general exponential-time reductions, due to the use of subset construction procedure, from the covert actuator attacker synthesis problems to the Ramadge-Wonham supervisor synthesis problems. It then follows that it is possible to use the many techniques and tools already developed for solving the supervisor synthesis problem to solve the covert actuator attacker synthesis problem for free. In particular, we show that, if the attacker cannot attack unobservable events to the supervisor, then the reductions can be carried out in polynomial time. We also provide a brief discussion on some other conditions under which the exponential blowup in state size can be avoided. Finally, we show how the reduction based synthesis procedure can be extended for the synthesis of successful covert actuator attackers that also eavesdrop the control commands issued by the supervisor.Comment: The paper has been accepted for the journal Discrete Event Dynamic System