118,487 research outputs found
The Responsibility Quantification (ResQu) Model of Human Interaction with Automation
Intelligent systems and advanced automation are involved in information
collection and evaluation, in decision-making and in the implementation of
chosen actions. In such systems, human responsibility becomes equivocal.
Understanding human casual responsibility is particularly important when
intelligent autonomous systems can harm people, as with autonomous vehicles or,
most notably, with autonomous weapon systems (AWS). Using Information Theory,
we develop a responsibility quantification (ResQu) model of human involvement
in intelligent automated systems and demonstrate its applications on decisions
regarding AWS. The analysis reveals that human comparative responsibility to
outcomes is often low, even when major functions are allocated to the human.
Thus, broadly stated policies of keeping humans in the loop and having
meaningful human control are misleading and cannot truly direct decisions on
how to involve humans in intelligent systems and advanced automation. The
current model is an initial step in the complex goal to create a comprehensive
responsibility model, that will enable quantification of human causal
responsibility. It assumes stationarity, full knowledge regarding the
characteristic of the human and automation and ignores temporal aspects.
Despite these limitations, it can aid in the analysis of systems designs
alternatives and policy decisions regarding human responsibility in intelligent
systems and advanced automation
Dijet Event Shapes as Diagnostic Tools
Event shapes have long been used to extract information about hadronic final
states and the properties of QCD, such as particle spin and the running
coupling. Recently, a family of event shapes, the angularities, has been
introduced that depends on a continuous parameter. This additional
parameter-dependence further extends the versatility of event shapes. It
provides a handle on nonperturbative power corrections, on non-global
logarithms, and on the flow of color in the final state.Comment: 18 pages, 3 figure
On the negative relation between investment-cash flow sensitivities and cash-cash flow.
We predict and find empirical support for a negative relation between the firmâs investment-cash flow sensitivity and cash-cash flow sensitivity, two measures suggested to capture the concept of financing constraints. This negative relation on the firm-level stems from the fact that both investments and the cash account are uses of funds competing for limited available cash flows. Additionally, we find that the investment-cash flow sensitivity is a better predictor for the firmâs constraint-status than the cash-cash flow sensitivity for a longitudinal sample of 1,233 U.S.-based listed firms using an evaluative framework based upon ex-post evaluation of the firmvarying sensitivities.financing constraints; investment-cash flow sensitivities; cash-cash flow sensitivities; firm-varying sensitivities;
Mixed-Signal Testability Analysis for Data-Converter IPs
In this paper, a new procedure to derive testability measures is presented. Digital testability can be calculated by means of probability, while in analog it is possible to calculate testability using impedance values. Although attempts have been made to reach compatibility, matching was somewhat arbitrary and therefore not necessarily compatible. The concept of the new approach is that digital and analog can be integrated in a more consistent way. More realistic testability figures are obtained, which makes testability of true mixed-signal systems and circuits feasible. To verify the results, our method is compared with a sensitivity analysis, for a simple 3-bit ADC
Extreme value laws in dynamical systems under physical observables
Extreme value theory for chaotic dynamical systems is a rapidly expanding
area of research. Given a system and a real function (observable) defined on
its phase space, extreme value theory studies the limit probabilistic laws
obeyed by large values attained by the observable along orbits of the system.
Based on this theory, the so-called block maximum method is often used in
applications for statistical prediction of large value occurrences. In this
method, one performs inference for the parameters of the Generalised Extreme
Value (GEV) distribution, using maxima over blocks of regularly sampled
observations along an orbit of the system. The observables studied so far in
the theory are expressed as functions of the distance with respect to a point,
which is assumed to be a density point of the system's invariant measure.
However, this is not the structure of the observables typically encountered in
physical applications, such as windspeed or vorticity in atmospheric models. In
this paper we consider extreme value limit laws for observables which are not
functions of the distance from a density point of the dynamical system. In such
cases, the limit laws are no longer determined by the functional form of the
observable and the dimension of the invariant measure: they also depend on the
specific geometry of the underlying attractor and of the observable's level
sets. We present a collection of analytical and numerical results, starting
with a toral hyperbolic automorphism as a simple template to illustrate the
main ideas. We then formulate our main results for a uniformly hyperbolic
system, the solenoid map. We also discuss non-uniformly hyperbolic examples of
maps (H\'enon and Lozi maps) and of flows (the Lorenz63 and Lorenz84 models).
Our purpose is to outline the main ideas and to highlight several serious
problems found in the numerical estimation of the limit laws
A mathematical model for breath gas analysis of volatile organic compounds with special emphasis on acetone
Recommended standardized procedures for determining exhaled lower respiratory
nitric oxide and nasal nitric oxide have been developed by task forces of the
European Respiratory Society and the American Thoracic Society. These
recommendations have paved the way for the measurement of nitric oxide to
become a diagnostic tool for specific clinical applications. It would be
desirable to develop similar guidelines for the sampling of other trace gases
in exhaled breath, especially volatile organic compounds (VOCs) which reflect
ongoing metabolism. The concentrations of water-soluble, blood-borne substances
in exhaled breath are influenced by: (i) breathing patterns affecting gas
exchange in the conducting airways; (ii) the concentrations in the
tracheo-bronchial lining fluid; (iii) the alveolar and systemic concentrations
of the compound. The classical Farhi equation takes only the alveolar
concentrations into account. Real-time measurements of acetone in end-tidal
breath under an ergometer challenge show characteristics which cannot be
explained within the Farhi setting. Here we develop a compartment model that
reliably captures these profiles and is capable of relating breath to the
systemic concentrations of acetone. By comparison with experimental data it is
inferred that the major part of variability in breath acetone concentrations
(e.g., in response to moderate exercise or altered breathing patterns) can be
attributed to airway gas exchange, with minimal changes of the underlying blood
and tissue concentrations. Moreover, it is deduced that measured end-tidal
breath concentrations of acetone determined during resting conditions and free
breathing will be rather poor indicators for endogenous levels. Particularly,
the current formulation includes the classical Farhi and the Scheid series
inhomogeneity model as special limiting cases.Comment: 38 page
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