A Simple Calculation in Service of Constraining the Rate of FU Orionis Outburst Events from Photometric Monitoring Surveys

An enigmatic and rare type of young stellar object is the FU Orionis class. The members are interpreted as "outbursting," that is, currently in a state of enhanced accretion by several orders of magnitude relative to the more modest disk-to-star accretion rates measured in typical T Tauri stars. They are key to our understanding of the history of stellar mass assembly and pre-main sequence evolution, as well as critical to consider in the chemical and physical evolution of the circumstellar environment -- where planets form. A common supposition is that *all* T Tauri stars undergo repeated such outbursts, more frequently in their earlier evolutionary stages when the disks are more massive, so as to build up the requisite amount of stellar mass on the required time scale. However, the actual data supporting this traditional picture of episodically enhanced disk accretion are limited, and the observational properties of the known sample of FU Ori objects quite diverse. To improve our understanding of these rare objects, we outline the logic for meaningfully constraining the rate of FU Ori outbursts and present numbers to guide parameter choices in the analysis of time domain surveys.Comment: accepted for publication in Ap

Fault Diagnosis for Polynomial Hybrid Systems

Safety requirements of technological processes trigger an increased demand for elaborate fault diagnosis tools. However, abrupt changes in system behavior are hard to formulate with continuous models but easier to represent in terms of hybrid systems. Therefore, we propose a set-based approach for complete fault diagnosis of hybrid polynomial systems formulated as a feasibility problem. We employ mixed-integer linear program relaxation of this formulation to exploit the presence of discrete variables. We improve the relaxation with additional constraints for the discrete variables. The efficiency of the method is illustrated with a simple two-tank example subject to multiple faults

Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool

Model predictive control (MPC) is an optimization-based strategy for high-performance control that is attracting increasing interest. While MPC requires the online solution of an optimization problem, its ability to handle multivariable systems and constraints makes it a very powerful control strategy specially for MPC of embedded systems, which have an ever increasing amount of sensing and computation capabilities. We argue that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems. The main burden for the implementation of MPC on FPGAs is the challenging design of the necessary algorithms. We outline an approach to achieve a software-supported optimized implementation of MPC on FPGAs using high-level synthesis tools and automatic code generation. The proposed strategy exploits the arithmetic operations necessaries to solve optimization problems to tailor an FPGA design, which allows a tradeoff between energy, memory requirements, cost, and achievable speed. We show the capabilities and the simplicity of use of the proposed methodology on two different examples and illustrate its advantages over a microcontroller implementation

A novel nuclear genetic code alteration in yeasts and the evolution of codon reassignment in eukaryotes.

The genetic code is the cellular translation table for the conversion of nucleotide sequences into amino acid sequences. Changes to the meaning of sense codons would introduce errors into almost every translated message and are expected to be highly detrimental. However, reassignment of single or multiple codons in mitochondria and nuclear genomes, although extremely rare, demonstrates that the code can evolve. Several models for the mechanism of alteration of nuclear genetic codes have been proposed (including "codon capture," "genome streamlining," and "ambiguous intermediate" theories), but with little resolution. Here, we report a novel sense codon reassignment in Pachysolen tannophilus, a yeast related to the Pichiaceae. By generating proteomics data and using tRNA sequence comparisons, we show that Pachysolen translates CUG codons as alanine and not as the more usual leucine. The Pachysolen tRNACAG is an anticodon-mutated tRNA(Ala) containing all major alanine tRNA recognition sites. The polyphyly of the CUG-decoding tRNAs in yeasts is best explained by a tRNA loss driven codon reassignment mechanism. Loss of the CUG-tRNA in the ancient yeast is followed by gradual decrease of respective codons and subsequent codon capture by tRNAs whose anticodon is not part of the aminoacyl-tRNA synthetase recognition region. Our hypothesis applies to all nuclear genetic code alterations and provides several testable predictions. We anticipate more codon reassignments to be uncovered in existing and upcoming genome projects

Control and Coordination in Hierarchical Systems

This book presents the applied theory of control and cooordination in hierarchical systems which are those where decision making has been divided in a certain way. It concentrates on various aspects of optimal control in large scale systems and covers a range of topics from multilevel methods for optimizing by interactive feedback procedures to methods for sequential, hierarchical control in large dynamic systems

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity

Planets Around Low-Mass Stars (PALMS). II. A Low-Mass Companion to the Young M Dwarf GJ 3629 Separated By 0.2"

We present the discovery of a 0.2" companion to the young M dwarf GJ 3629 as part of our high contrast adaptive optics imaging search for giant planets around low-mass stars with the Keck-II and Subaru telescopes. Two epochs of imaging confirm the pair is co-moving and reveal signs of orbital motion. The primary exhibits saturated X-ray emission, which together with its UV photometry from GALEX point to an age younger than ~300 Myr. At these ages the companion lies below the hydrogen burning limit with a model-dependent mass of 46 +/- 16 Mjup based on the system's photometric distance of 22 +/- 3 pc. Resolved YJHK photometry of the pair indicates a spectral type of M7 +/- 2 for GJ 3629 B. With a projected separation of 4.4 +/- 0.6 AU and an estimated orbital period of 21 +/- 5 yr, GJ 3629 AB is likely to yield a dynamical mass in the next several years, making it one of only a handful of brown dwarfs to have a measured mass and an age constrained from the stellar primary.Comment: Accepted for publication in Ap