Resource-aware IoT Control: Saving Communication through Predictive Triggering

The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.Comment: 16 pages, 15 figures, accepted article to appear in IEEE Internet of Things Journal. arXiv admin note: text overlap with arXiv:1609.0753

Education and Optimal Dynamic Taxation

We study optimal tax and educational policies in a dynamic private information economy, in which ex-ante heterogeneous individuals make an educational investment early in their life and face a stochastic wage distribution. We characterize labor and education wedges in this setting analytically and numerically, using a calibrated example. We present ways to implement the optimum. In one implementation there is a common labor income tax schedule, and a repayment schedule for government loans given out to agents during education. These repayment plans are contingent on loan size and income and capture the history dependence of the labor wedges. Applying the model to US-data and a binary education decision (graduating from college or not) we characterize optimal labor wedges for individuals without college degree and with college degree. The labor wedge of college graduates as a function of income lies first strictly above their counterparts from high-school, but this reverses at higher incomes. The loan repayment schedule is hump-shaped in income for college graduates.optimal dynamic taxation, education, implementation

Education and optimal dynamic taxation: The role of income-contingent student loans

We study Pareto optimal tax and education policies when human capital upon labor market entry is endogenous and individuals face wage uncertainty. Though optimal labor distortions are history-dependent, i.e. depend on income and education, simple policy instruments can yield the desired distortions: a single nonlinear labor income tax schedule combined with income-contingent loans. To take themodel to the (US) data, we simplify the model to a binary education decision (graduating from college or not). We find that for lowand intermediate incomes the labor supply decision of college graduates should be distorted more heavily than for individuals without a college degree. As a consequence, the optimal student loan repayment schedule increases in income for this range. This result holds along the Pareto frontier. We compare the second best to a situation where loan repayment is restricted to be independent from income and find significant welfare gains.Optimal dynamic taxation, education, implementation

Generating Correlated Ordinal Random Values

Ordinal variables appear in many field of statistical research. Since working with simulated data is an accepted technique to improve models or test results there is a need for providing correlated ordinal random values with certain properties like marginal distribution and correlation structure. The present paper describes two methods for generating such values: binary conversion and a mean mapping approach. The algorithms of the two methods are described and some examples of the outcomes are shown

Sum-frequency ionic Raman scattering

In a recent report sum-frequency excitation of a Raman-active phonon was experimentally demonstrated for the first time. This mechanism is the sibling of impulsive stimulated Raman scattering, in which difference-frequency components of a light field excite a Raman-active mode. Here we propose that ionic Raman scattering analogously has a sum-frequency counterpart. We compare the four Raman mechanisms, photonic and ionic difference- and sum-frequency excitation, for three different example materials using a generalized oscillator model for which we calculate the parameters with density functional theory. Sum-frequency ionic Raman scattering completes the toolkit for controlling materials properties by means of selective excitation of lattice vibrations

Squark production in R-symmetric SUSY with Dirac gluinos: NLO corrections

R-symmetry leads to a distinct realisation of SUSY with a significantly modified coloured sector featuring a Dirac gluino and a scalar colour octet (sgluon). We present the impact of R-symmetry on squark production at the 13 TeV LHC. We study the total cross sections and their NLO corrections from all strongly interacting states, their dependence on the Dirac gluino mass and sgluon mass as well as their systematics for selected benchmark points. We find that tree-level cross sections in the R-symmetric model are reduced compared to the MSSM but the NLO K-factors are generally larger in the order of ten to twenty per cent. In the course of this work we derive the required DREG $\to$ DRED transition counterterms and necessary on-shell renormalisation constants. The real corrections are treated using FKS subtraction, with results cross checked against an independent calculation employing the two cut phase space slicing method.Comment: 46 pages, 15 figures; updated to match published versio

Event-triggered Pulse Control with Model Learning (if Necessary)

In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel event-triggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control.Comment: Accepted final version to appear in: Proc. of the American Control Conference, 201